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Data-Driven Approaches to Assist Achieving Sustainable Development for Nations.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-Driven Approaches to Assist Achieving Sustainable Development for Nations./
作者:	Shuai, Chenyang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	152 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Environmental studies. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29030870
ISBN:	9798780610083

Data-Driven Approaches to Assist Achieving Sustainable Development for Nations.
Shuai, Chenyang.

Data-Driven Approaches to Assist Achieving Sustainable Development for Nations. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 152 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--University of Michigan, 2021.

This item must not be sold to any third party vendors.

The United Nations set 17 Sustainable Development Goals (SDGs) in 2015 as a universal call aiming to end poverty, protect the planet, and ensure that all people enjoy peace and prosperity by 2030. These 17 SDGs are committed by 193 countries and regions to transform the conventional development agenda for sustainable development. My dissertation focuses on using data-driven approaches to address some of the challenges in SDG implementation for nations, including challenges in data collection, performance comparison, and prediction. To monitor the progress towards achieving SDGs, the 17 goals are underpinned by 169 targets which are measured by an even larger number of SDG indicators. The sheer number of SDG indicators makes data collection a critical challenge. My dissertation begins with identifying the principal indicators, the changes of which can represent the variations of the majority of SDG indicators with the lowest difficulty of data collection. Integrating principal component analysis and multiple regression, I identify 147 principal indicators that can explain at least 90% of the annual variation of 351 SDG indicators. My results can guide future investment in the data infrastructure for SDG monitoring by giving priorities to these principal indicators for global comparison. Per capita based metrics, such as GDP per capita, are widely used in SDG performance comparison, which assumes stock measures (e.g., GDP) scale linearly with population. However, this assumption does not always hold since it ignores the effect of agglomeration resulting from non-linear interactions in social dynamics. I find extensive empirical evidence that many important national development indicators scale non-linearly with population size, which provides a quantitative argument against the mainstream practice to compare national development using per-capita measures. I further propose a quantitative framework to explain the scaling in nations originating from the scaling in cities. The global progress to achieve the SDGs by 2030 has been stalled by the coronavirus disease 2019 (COVID-19) pandemic. Several studies have qualitatively assessed the impacts of COVID-19 on SDGs. Quantitative assessments, however, are rare, largely due to the complex non-linear relationship among SDG indicators making prediction difficult. I use machine learning approaches to capture the complex non-linear relationship between SDG indicators and evaluate the impacts of COVID-19 on SDGs. I find that the overall SDG performance declined by 7.7% in 2020 at the global scale, with the performance of 12 socioeconomic SDGs decreasing by 3.0-22.3% and that of 4 environmental SDGs increasing by 1.6-9.2%. By 2024, the progress of 12 SDGs will lag behind for one to eight years compared to their pre-COVID-19 trajectories, while extra time will be gained for 4 environment-related SDGs. Furthermore, the pandemic will cause more impact on emerging market and developing economy than on advanced economy, and the latter will recover more quickly to be close to their pre-COVID-19 trajectories by 2024.

ISBN: 9798780610083Subjects--Topical Terms:

2122803
Environmental studies.
Subjects--Index Terms:

Sustainable Development Goals

Data-Driven Approaches to Assist Achieving Sustainable Development for Nations.
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520 $a The United Nations set 17 Sustainable Development Goals (SDGs) in 2015 as a universal call aiming to end poverty, protect the planet, and ensure that all people enjoy peace and prosperity by 2030. These 17 SDGs are committed by 193 countries and regions to transform the conventional development agenda for sustainable development. My dissertation focuses on using data-driven approaches to address some of the challenges in SDG implementation for nations, including challenges in data collection, performance comparison, and prediction. To monitor the progress towards achieving SDGs, the 17 goals are underpinned by 169 targets which are measured by an even larger number of SDG indicators. The sheer number of SDG indicators makes data collection a critical challenge. My dissertation begins with identifying the principal indicators, the changes of which can represent the variations of the majority of SDG indicators with the lowest difficulty of data collection. Integrating principal component analysis and multiple regression, I identify 147 principal indicators that can explain at least 90% of the annual variation of 351 SDG indicators. My results can guide future investment in the data infrastructure for SDG monitoring by giving priorities to these principal indicators for global comparison. Per capita based metrics, such as GDP per capita, are widely used in SDG performance comparison, which assumes stock measures (e.g., GDP) scale linearly with population. However, this assumption does not always hold since it ignores the effect of agglomeration resulting from non-linear interactions in social dynamics. I find extensive empirical evidence that many important national development indicators scale non-linearly with population size, which provides a quantitative argument against the mainstream practice to compare national development using per-capita measures. I further propose a quantitative framework to explain the scaling in nations originating from the scaling in cities. The global progress to achieve the SDGs by 2030 has been stalled by the coronavirus disease 2019 (COVID-19) pandemic. Several studies have qualitatively assessed the impacts of COVID-19 on SDGs. Quantitative assessments, however, are rare, largely due to the complex non-linear relationship among SDG indicators making prediction difficult. I use machine learning approaches to capture the complex non-linear relationship between SDG indicators and evaluate the impacts of COVID-19 on SDGs. I find that the overall SDG performance declined by 7.7% in 2020 at the global scale, with the performance of 12 socioeconomic SDGs decreasing by 3.0-22.3% and that of 4 environmental SDGs increasing by 1.6-9.2%. By 2024, the progress of 12 SDGs will lag behind for one to eight years compared to their pre-COVID-19 trajectories, while extra time will be gained for 4 environment-related SDGs. Furthermore, the pandemic will cause more impact on emerging market and developing economy than on advanced economy, and the latter will recover more quickly to be close to their pre-COVID-19 trajectories by 2024.
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